Studies of Welfare Populations: Data Collection and Research Issues. Common Nonresponse Adjustment Methods in Surveys

06/01/2002

In spite of the best strategies for collecting data from sampled units, nonresponse nearly always occurs in population surveys, including those of low-income families. A ''nonrespondent" is any sampled unit that is eligible for the study but for which data are not obtained for any reason. Failure to match the sample cases with the administrative files used to gather outcome data, refusal to participate in the survey, or situations such as ''not-at-home after multiple calls," ''language problems," and ''knowledgeable person not available" are some of the reasons why an eligible sampled unit may not participate in a survey. On the other hand, sampled units that are ineligible for the survey are not considered nonrespondents, even though they do not provide survey data. As discussed later in this section, nonrespondents and ineligibles are treated differently in the nonresponse adjustment process.

When nonresponse is present, a weight adjustment can partially compensate for the loss of data. This weight adjustment increases the weights of the sampled cases for which data were collected. The first step in adjusting for nonresponse is the construction of weighting classes. As discussed in the following text, within each weighting class, the base weights are inflated by the inverse of the response rate so that the sum of the adjusted base weights for respondents is equal to the sum of the base weights for the total eligible sample selected in the weighting class. Returning to the FIS example, assume that 160 families were selected (with equal probability) within a weighting class and that 77 families responded to the survey. Because the weight for each family is equal to 50 (as shown earlier), the nonresponse-adjusted weight is about 104 (i.e., 50 multiplied by 160/77). Thus, after nonresponse adjustment each responding family in the sample represents about 104 families in the population within the weighting class.

The effectiveness of nonresponse adjustment procedures in reducing nonresponse bias is directly related to the ability to construct appropriate nonresponse adjustment classes. The following subsection provides a brief summary of two procedures commonly used to construct adjustment classes. The next subsection discusses sample-based adjustment procedures that are commonly used to compensate for nonresponse. Then we describe population-based adjustment procedures (poststratification and raking) that are widely used for noncoverage adjustment, or sometimes used to correct simultaneously for both nonresponse and noncoverage. Additional benefits of population-based adjustments include reduction in the sampling errors of the sample estimates as well as achieving consistency with the known population counts. In poststratification and raking, respondents are categorized according to one or more variables (e.g., age, gender, race, or income level) at a recent point in time, and the survey estimates are benchmarked to the known population totals. For a general review of weighting for nonresponse, refer to Elliot (1991). Finally, we provide a discussion of the importance of balancing bias and variance when adjusting survey data.

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